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Rai, N
N.L., R
Ashworth, A
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Authors
Shanwad, U
H, V
N.L., R
Kanannnavar, P.S
Swamy, S
Patil, M.B
Rai, N
Zhang, Y
Quanbeck, J
Christensen, A
Sun, X
Ashworth, A
Kharel, T
Owens, P
Topics
Precision Nutrient Management
Big Data, Data Mining and Deep Learning
Small Holders and Precision Agriculture
Type
Poster
Oral
Year
2012
2022
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1. Precision Nutrient Management in Cotton- A Case Study from India

Cotton is being one of the important commercial crops in India, farmers have adopted cultivating hybrid cotton to achieve higher yield. In this context, cotton is becoming input intensive crop... U. Shanwad, V. H, R. N.l., P.S. Kanannnavar, S. Swamy, M.B. Patil

2. Spotweeds: a Multiclass UASs Acquired Weed Image Dataset to Facilitate Site-specific Aerial Spraying Application Using Deep Learning

Unmanned aerial systems (UASs)-based spot spraying application is considered a boon in Precision Agriculture (PA). Because of spot spraying, the amount of herbicide usage has reduced significantly resulting in less water contamination or crop plant injury. In the last demi-decade, Deep Learning (DL) has displayed tremendous potential to accomplish the task of identifying weeds for spot spraying application. Also, most of the ground-based weed management technologies have relied on DL techniques... N. Rai, Y. Zhang, J. Quanbeck, A. Christensen, X. Sun

3. Evaluating How Operator Experience Level Affects Efficiency Gains for Precision Agricultural Tools

Tractor guidance (TG) improve environmental gains relative to non-precision technologies; however, studies evaluating how tractor operator experience for non-guidance comparisons impact gains are nonexistent. This study explores spatial relationships of overlaps and gaps with operator experience level (0-1; 2-3; 6+ years) during fertilizer and herbicide applications based on terrain attributes.  Tractor paths recorded by global navigation satellite systems were used to create overlap polygons.... A. Ashworth, T. Kharel, P. Owens